Tourism industry operation behavior tracking, storage and analysis system based on big data

Abstract

The invention provides a tourism industry operation behavior tracking, storage and analysis system based on big data, relates to the technical field of tourism industry, and adopts a convolutional neural network (CNN) to deeply fuse multi-source heterogeneous data such as texts, images, voices and physiological signals to identify multi-dimensional fine-grained emotions of tourists in real time. The system accurately quantifies the causal effect of the operation behavior on the tourist emotion by using a causal inference model, and reveals deep inducements. On the basis, knowledge reasoning and reinforcement learning are combined, the system can generate context-aware, personalized and explainable active optimization suggestions, and passenger flow distribution, service pushing and product iteration are covered. In addition, the system has digital twinborn construction and federal learning cooperation capabilities. According to the method, data privacy is ensured, transformation from traditional passive analysis to active prediction and intelligent intervention is realized, the tourist satisfaction degree and the resource utilization rate are remarkably improved, and intelligent development of the tourism industry is facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tourism industry, in particular to a tourism industry operation behavior tracking, storage and analysis system based on big data. BACKGROUND

[0002] The current operation and management of the tourism industry faces many challenges. Traditional operation methods rely on lagging manual statistics and limited questionnaire surveys, making it difficult to obtain real-time and comprehensive feedback on tourists' real experiences and emotions. Although existing big data analysis systems can handle large amounts of data, they generally have the problem of data silos, making it difficult to integrate heterogeneous data from different channels and modalities of text, images, speech and physiological signals, resulting in fragmented and superficial understanding of tourists' behavior and emotions. In addition, most systems focus on post-event analysis, lack real-time perception and dynamic prediction capabilities for tourists' emotions, and cannot effectively identify the deep causal relationships that lead to tourists' satisfaction and dissatisfaction, making operation decisions often based on experience rather than data-driven scientific insights. This information lag and lack of analysis capabilities seriously hinder the efficiency of tourism enterprises in improving service quality, optimizing resource allocation and responding to unexpected situations, making it difficult to achieve a transformation from passive response to active optimization and personalized services, thereby affecting tourists' overall experience and the sustainable development of the industry. SUMMARY

[0003] Technical problems to be solved

[0004] In view of the deficiencies in the prior art, the present application provides a tourism industry operation behavior tracking, storage and analysis system based on big data, which solves the problems of the prior art.

[0005] (II) Technical solutions

[0006] The present application provides the following technical solutions: a tourism industry operation behavior tracking, storage and analysis system based on big data, comprising: an intelligent perception layer: for deploying multi-source heterogeneous data collection probes, real-time collection of multi-modal tourism-related data including structured data and unstructured data, the collection process can be combined with edge computing to realize real-time processing and preliminary feature extraction;

[0007] Data intelligence hub: for fusion, preprocessing, storage and management of data collected by the intelligent perception layer, and construction of a semantic knowledge system in the tourism field, the core components of the data intelligence hub include: multi-modal emotion computing and causal inference unit: for performing the following operations: multi-modal data feature extraction and alignment, multi-dimensional emotion state real-time identification, potential cause and effect relationship inference and experience quality active optimization suggestion generation;

[0008] Intelligent decision engine: for receiving and executing the optimization suggestions generated by the multi-modal sentiment computing and causal inference unit, and can combine reinforcement learning and simulation for autonomous decision optimization;

[0009] Service application layer: for providing the emotion analysis results, causal inference reports and optimization suggestions to operators, tourists and other third parties through user interfaces and API interfaces.

[0010] Preferably, in the multi-modal data feature extraction and alignment process, the cross-modal attention mechanism aims to improve the model's attention to key information when fusing different modal information.

[0011] Preferably, the multi-dimensional emotion state real-time identification unit can identify the micro-emotion fluctuations of tourists and the macro-group emotion trends.

[0012] Preferably, in the potential cause and effect relationship inference, the causal inference model can distinguish between correlation and causality, and avoid misjudgment.

[0013] Preferably, in the experience quality active optimization suggestion generation process, the knowledge reasoning engine and reinforcement learning model can continuously learn and optimize the generation strategy of the suggestion based on historical operation data and feedback.

[0014] Preferably, it includes the following steps:

[0015] Sp1: Multi-modal tourism data collection: Real-time collection of multi-modal tourism-related data including structured data and unstructured data;

[0016] Sp2: Multi-modal data feature extraction and alignment: Deep feature extraction is performed on the collected different modal data respectively and jointly, and multi-modal fusion network and cross-modal attention mechanism are used to accurately align and fuse the extracted multi-modal features in time and semantics;

[0017] Sp3: Multi-dimensional emotion state real-time identification: Based on the fused multi-modal representation, the multi-dimensional and fine-grained emotion state of the tourism user and group is identified in real time;

[0018] Sp4: Potential cause and effect relationship inference: For the identified emotion state changes, the causal inference model is used to analyze and quantify the causal relationship and action path between specific operation behavior and tourist emotion state, and identify the key causes leading to emotion changes;

[0019] Sp5: Experience quality active optimization suggestion generation and execution: According to the emotion state identification results and potential cause and effect relationship inference results, context-aware, personalized and explainable experience quality active optimization suggestions are generated, and the suggestions are executed and pushed through the intelligent decision engine.

[0020] Preferably, the emotional state identified in Sp3 includes tracking the emotional changes of tourists at different time points and different geographical locations.

[0021] Preferably, the key inducements identified in Sp4 include direct inducements and indirect inducements.

[0022] (III) Beneficial effects

[0023] The present application has the following beneficial effects:

[0024] 1. Through multi-modal data feature extraction and alignment, the system can break through the limitations of traditional data analysis, deeply integrate various heterogeneous data sources such as text, image, voice, and physiological signals, form a more comprehensive and accurate understanding of tourist experience, and avoid the one-sidedness that may be caused by a single data source. Secondly, the system has multi-dimensional emotional state real-time recognition capability, which can capture the fine-grained emotional fluctuations of tourists in milliseconds, including satisfaction, anxiety, fatigue, etc., thereby realizing real-time perception and early warning of tourist experience, and changing passive response to active care. More importantly, the introduction of causal inference model enables the system to go beyond simple correlation analysis, accurately identify and quantify the direct and indirect causal effects of specific operational behaviors on tourist emotions. This enables operators to identify the root cause of the problem and make targeted optimizations rather than blind attempts. Finally, based on these deep insights, the system can intelligently generate context-aware, personalized, and interpretable experience quality proactive optimization recommendations, including negative emotion early warning, personalized service intervention, operational process adjustment, and product iteration direction, etc., greatly improving the intelligent level of tourism operation, the scientificity and efficiency of decision-making, and ultimately significantly improving the overall experience and satisfaction of tourists, promoting the high-quality development of the tourism industry. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 is a system flowchart of the present application;

[0026] Figure 2 is a system working step diagram of the present application;

[0027] Figure 3 is a multi-modal emotion recognition system structure diagram of the present application;

[0028] Figure 4 is a tourist emotion recognition flowchart of the present application. DETAILED DESCRIPTION

[0029] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0030] Embodiment one

[0031] Please refer to Figures 1-4 , the big data-based tourism industry operation behavior tracking, storage and analysis system, comprising: an intelligent sensing layer: for deploying multi-source heterogeneous data collection probes, collecting multi-modal tourism related data including structured data and unstructured data in real time, the collection process can combine edge computing to realize real-time processing and preliminary feature extraction;

[0032] Data intelligent hub: for fusion, preprocessing, storage and management of the data collected by the intelligent sensing layer, and construction of semantic knowledge system in the tourism field, the core components of the data intelligent hub include: multi-modal sentiment computing and causal inference unit: for performing the following operations: multi-modal data feature extraction and alignment, multi-dimensional emotion state real-time identification, potential cause and effect relationship inference and experience quality active optimization suggestion generation;

[0033] Intelligent decision engine: for receiving and executing the optimization suggestions generated by the multi-modal sentiment computing and causal inference unit, and can combine reinforcement learning and simulation for autonomous decision optimization;

[0034] Service application layer: for providing emotion analysis results, causal inference reports and optimization suggestions to operators, tourists and other third parties through user interface and API interface.

[0035] In the process of multi-modal data feature extraction and alignment, the cross-modal attention mechanism aims to improve the model's attention to key information when fusing different modal information.

[0036] The multi-dimensional emotion state real-time identification unit can identify the microscopic emotional fluctuations and macroscopic group emotional trends of tourists.

[0037] In the potential cause and effect relationship inference, the causal inference model can distinguish between correlation and causality, avoiding misjudgment.

[0038] In the experience quality active optimization suggestion generation process, the knowledge reasoning engine and reinforcement learning model can continuously learn and optimize the generation strategy of the suggestion based on historical operation data and feedback.

[0039] Comprising the following steps:

[0040] Sp1: Multimodal tourism data collection: Real-time collection of multimodal tourism-related data including structured data and unstructured data;

[0041] Sp2: Multimodal data feature extraction and alignment: For different modal data collected, deep feature extraction is performed respectively and jointly, and cross-modal attention mechanism and multimodal fusion network are used to accurately align and fuse the extracted multimodal features in time and semantics;

[0042] Sp3: Multidimensional emotional state real-time recognition: Based on the fused multimodal representation, the multidimensional and fine-grained emotional state of the tourism user and group is recognized in real time;

[0043] Sp4: Inference of potential cause and effect relationship: Based on the identified emotional state change, a causal inference model is used to analyze and quantify the causal relationship and action path between specific operation behavior and tourist emotional state, and identify the key inducement leading to emotional change;

[0044] Sp5: Experience quality active optimization suggestion generation and execution: According to the emotional state recognition result and the potential cause and effect relationship inference result, the situation-aware, personalized and explainable experience quality active optimization suggestion is generated, and the suggestion is executed and pushed through the intelligent decision engine.

[0045] The emotional state identified in Sp3 includes tracking the emotional changes of tourists at different time points and different geographical locations.

[0046] The key inducements identified in Sp4 include direct inducements and indirect inducements.

[0047] Embodiment Two

[0048] Based on Embodiment One, please refer to Figure 2, The data intelligence hub pre-processes the multi-source heterogeneous data collected by the intelligent perception layer, which is a systematic process aimed at ensuring data quality, consistency, and availability. It first performs standardization and normalization operations on structured data. At the same time, for unstructured data such as text, images, and speech, preliminary cleaning is performed to remove noise, fill in missing segments, and convert these data into formats that can be processed by subsequent deep learning models. Image pixel normalization and speech signal denoising are also involved. In addition, preprocessing also involves outlier detection and processing, identifying and correcting and marking data points that deviate significantly from the normal range to prevent their negative impact on subsequent analysis. Through these steps, the data intelligence hub ensures that all data is of high quality, clean, and uniform before entering the advanced analysis and modeling stage, laying a solid foundation for subsequent deep fusion and causal inference. The data intelligence hub uses a multi-modal fusion network based on convolutional neural networks (CNN) to accurately align and deeply fuse the deep features of different modalities such as text, images, speech, and physiological signals collected by the intelligent perception layer in time and semantics, forming a unified and comprehensive multi-modal representation.

[0049] Multi-modal data feature extraction and alignment: Different modal data collected by the intelligent perception layer includes but is not limited to text, image, voice, and physiological signal. Deep feature extraction is performed respectively and jointly. A text encoder based on a convolutional neural network (CNN) is used for semantic feature extraction. A convolutional neural network (CNN) is used to extract image visual features. An acoustic model based on a convolutional neural network (CNN) is used to extract voice timing features. Cross-modal attention mechanism and multi-modal fusion network are used to accurately align and fuse the extracted multi-modal features in time and semantics to form unified multi-modal representation. Multi-dimensional emotional state real-time recognition: Based on the fused multi-modal representation, a multi-label classification model and a regression model deep neural network combined with an attention mechanism are used to identify the multi-dimensional and fine-grained emotional state of the tourist user and group in real time. The emotional state includes but is not limited to satisfaction, excitement, fatigue, anxiety, curiosity, surprise, disappointment, and frustration. The corresponding confidence score can be output. Potential cause and effect relationship inference: For the identified emotional state change, a causal inference model is used to analyze and quantify the causal relationship and action path between specific operation behavior variables, including queuing time, guide content, environmental noise, service personnel attitude, facility comfort, information transparency, and tourist emotional state. The key causes leading to the generation, change, and disappearance of positive and negative emotions are identified, and the causal effect strength of each cause can be output. Experience quality active optimization suggestion generation: According to the multi-dimensional emotional state recognition result and the potential cause and effect relationship inference result, combined with the preset operation target and resource constraint, a knowledge reasoning engine and a reinforcement learning model are used to input data including the current emotional state of tourists, behavior data, environmental information, and historical intervention records. The system trains the reinforcement learning agent to learn what operation behavior to take under different tourist states to maximize overall tourist satisfaction. The output result is a set of optimal intervention strategies and behavior decision sequences, which are used to guide the real-time response of scenic spot services, and realize the maximization of tourist experience and the optimization of operation efficiency.

[0050] Multi-modal data deep fusion: is the basis of the whole unit. The system is not simply splicing data from different modalities, but using advanced deep feature extraction and cross-modal fusion technology. Specifically, for text data including but not limited to comments, forum posts, the system uses a convolutional neural network CNN-based text encoder for semantic encoding, capturing the deep meaning and emotional tendency of words, sentences, and even chapters. For image data, including but not limited to tourist selfies, scenic photos, and video clips, visual features are extracted through convolutional neural networks CNN, especially facial recognition and pose estimation techniques can be used to capture tourist facial micro-expressions happy, surprised, frustrated and body language tired, relaxed, which are key supplements to emotional recognition. For voice data, the system uses a convolutional neural network CNN-based acoustic model to analyze tone, speed, volume, and other acoustic features, and combines speech recognition technology for semantic understanding. In addition, for physiological signals collected through smart wearable devices, the system uses a customized convolutional neural network CNN to extract physiological indicators, which often directly reflect an individual's stress, excitement, and fatigue. After feature extraction, the key is cross-modal fusion and alignment. Because different modalities of data may differ in time and semantics, the system will use a convolutional neural network CNN-based multi-modal fusion network that allows the model to intelligently determine which modalities are more important in the current context and give them higher weights. When analyzing a tourist's comment about long queues, the model will pay more attention to negative words in the text, while combining the tourist's anxious expression in the video and the elevated heart rate in the physiological signal. This precise alignment and fusion of time and semantics ensures that the unified multi-modal representation formed can fully and seamlessly capture the comprehensive experience of tourists in a specific context.

[0051] Real-time multi-dimensional emotional state recognition: Based on the deeply fused multi-modal representation, the system enters the emotional recognition stage. Unlike traditional simple positive and negative emotion classification, the goal of this unit is to identify multi-dimensional, fine-grained emotional states. This is usually achieved through regression analysis using a convolutional neural network CNN-based multi-label classification model, which can simultaneously predict multiple emotional dimensions of tourists, including satisfaction, excitement, fatigue, anxiety, curiosity, surprise, disappointment, and frustration, and can output corresponding confidence scores. "Real-time" is the key here. With the help of streaming data processing framework combined with edge computing capabilities, the system can analyze new tourist data in real time, and when a large number of tourists exhibit fatigue and anxiety physiological signals and expressions at a certain scenic spot, the system can quickly identify and issue warnings. This real-time nature allows operators to respond quickly and avoid the accumulation and spread of negative emotions.

[0052] Causal Inference and Optimization Suggestion Generation: Emotion recognition is just "knowing that", while causal inference is "knowing why". Instead of just recognizing the "tourist dissatisfaction" phenomenon, the system aims to answer why the dissatisfaction happens, and what operation behaviors lead to the dissatisfaction. To achieve this, the system integrates a structural causal model (SCM) and a causal graph discovery algorithm: SCM allows the construction of a graph model representing the causal relationships between variables. Based on historical data, including operation records and tourist emotional feedback, the system can automatically discover and verify pre-defined causal paths through algorithms. Through causal inference methods, the system can accurately identify and quantify the direct and indirect causal effects and action paths between specific operation behaviors and tourist emotional states. This allows operators to understand the root cause of the problem rather than just focusing on surface phenomena, and to predict the emotional feedback that different operation interventions may bring. Finally, based on these in-depth causal insights, the system uses a knowledge reasoning engine to combine pre-defined business rules, industry best practices, and reinforcement learning models to optimize strategy generation. The system generates context-aware, personalized, and explainable experience quality proactive optimization suggestions by continuously learning from historical intervention effects and tourist feedback.

[0053] Direct Causes: Refers to operation behaviors and environmental factors that immediately and directly lead to changes in tourist emotions. When tourists feel anxious because of long waiting time, "long waiting time" is the direct cause of anxiety. In this case, operation behaviors directly affect tourists' emotions. Indirect Causes: Refers to factors that ultimately affect tourists' emotions by affecting other intermediate variables. When "lack of clear alternative route signs" leads to "increased fatigue" in tourists during route finding, and ultimately leads to dissatisfaction, "lack of clear alternative route signs" is an indirect cause. It indirectly affects the final emotion by affecting tourists' behavior confusion and physiological state of fatigue.

[0054] The structural causal model (SCM) collaborates with the causal graph discovery algorithm to uncover the deep causal relationships among variables from observational data. First, in the data preparation phase, the system collects and preprocesses multivariate data to ensure data quality and availability. Then, in the causal graph discovery phase, the algorithm analyzes the preprocessed data by systematically performing a series of conditional independence tests to identify the conditional independence and dependence relationships between variables. This process ultimately builds a causal graph that visually represents the direct causal connections between variables. Once the causal graph is discovered, it enters the structural causal model (SCM) construction and parameter estimation phase. In this phase, for each node in the causal graph, a structural equation is established, representing it as a function of its direct causes and an independent random error term. The specific form of these functions is chosen based on data characteristics and domain knowledge, and the parameters are estimated using statistical methods to quantify the strength and form of causal relationships. Finally, the constructed SCM is used for causal reasoning and intervention analysis, answering counterfactual and intervention questions such as "If X is forced to change, how will Y react?" This provides decision-makers with deep insights based on causal relationships, supporting more scientific and effective decision-making. The causal graph discovery algorithm in this system is used to automatically identify the causal relationship structure between variables from tourist multi-dimensional behavior data, environmental factors, emotional feedback, and physiological signals. The algorithm takes preprocessed structured samples as input, including variables such as queue time, service voice tone, tourist density, heart rate, and satisfaction, and outputs a causal structure graph in the form of a directed acyclic graph, clearly showing the causal paths between variables. This causal graph is widely used in the system for emotional cause analysis, operational optimization intervention strategy generation, and scene migration variable selection, supporting the system to achieve interpretable decision-making and intelligent operational management based on causal inference.

[0055] The cross-modal attention mechanism is a deep learning method for integrating information from different data modalities, including but not limited to text, images, speech, and physiological signals. This mechanism establishes dynamic attention weights between multi-modal data, allowing the model to automatically identify and strengthen the most valuable information in different modalities for the current task, thereby achieving more accurate fusion and alignment at the semantic level. In this invention, this mechanism is used to improve the accuracy of tourist emotion recognition, ensuring semantic consistency and temporal synchronization of heterogeneous information such as text reviews, facial expressions, and voice tones during the fusion process.

[0056] Structural Causal Model (SCM) is a modeling method combining mathematical functions and causal graphs, which is used to represent the causal relationship between variables. Unlike traditional correlation analysis, structural causal model can not only reveal "which factors are related", but also infer "which factors are the root cause of change". In this invention, SCM is used to analyze tourism operation behavior, including but not limited to the direct and indirect impact of queuing time, service frequency, etc. on the emotional state of tourists, to support the system to automatically identify key inducements and generate targeted optimization strategies, and to improve the scientific nature and effectiveness of operation intervention.

[0057] Embodiment three

[0058] Based on embodiment one, please refer to Figures 3-4 From single modality to multi-modal deep fusion of tourist sentiment understanding: existing tourism big data systems mostly stay in the analysis of single modality such as text comments, transaction data, etc., and only simply superimpose different modal data. After introducing multi-modal sentiment computing and causal inference unit, deep, real-time, multi-dimensional perception of tourist sentiment is realized. We not only extract features from heterogeneous data such as text, image facial micro-expression, posture, voice, and physiological signal heart rate variability, skin electric response through a unified convolutional neural network (CNN) framework, but more importantly, through the multi-modal fusion network based on CNN, we realize the precise alignment and deep fusion of these features in time and semantics. This fusion is not a simple splicing, but through learning the internal correlation and complementary information between different modalities, a unified representation that can fully reflect the complex emotional state of tourists is formed. This cross-modal deep fusion capability enables the system to capture subtle emotional changes that cannot be detected by a single modality. When the text comments of tourists seem calm, but their facial expressions and physiological indicators have shown fatigue and anxiety, the system can accurately identify it. This greatly improves the comprehensiveness and accuracy of understanding of tourist experience.

[0059] Operational optimization from correlation analysis to causal inference: Traditional data analysis often stops at correlation analysis between variables, i.e. "what happens at the same time", but cannot reveal the underlying causal relationship, i.e. "what causes what". The creative breakthrough of this solution lies in the integration of causal inference models. This enables the system to accurately identify and quantify the direct and indirect causal effects of specific operational behaviors such as queue length, tour content, and service personnel attitude on visitor emotional state satisfaction and anxiety from massive operational data and visitor feedback. The system not only discovers that "long queue time" is related to "low visitor satisfaction", but also quantifies the specific causal action strength of "queue time increasing X minutes, visitor satisfaction decreasing Y%" through causal inference, and even identifies the key link leading to this decrease. This ability enables operators to understand the deep causes of problems, thus formulating targeted and predictable optimization strategies, fundamentally solving operational pain points, and avoiding blind attempts and resource waste. This is a qualitative leap from "finding problems" to "solving problems", and is a core capability that most existing systems do not have.

[0060] From passive response to active prediction and intelligent intervention in operational mode transformation: Most existing systems are post-reporting tools, and operational decisions often lag behind. The creativity of this solution lies in the transformation of operational mode from passive response to active prediction and intelligent intervention. Through real-time multi-dimensional emotion recognition, the system can early warn of negative emotions of visitors and intervene before emotions accumulate and spread. Further, combined with causal inference, the system can predict the emotional changes that may be brought by certain operational adjustments, and generate situation-aware, personalized and interpretable optimization suggestions based on this. These suggestions are not just data reports, but directly executable action plans such as intelligent passenger flow diversion, personalized service push, and emergency response strategy optimization. The system can also continuously learn and optimize the generation strategy of these suggestions through reinforcement learning, so that it becomes more and more in line with actual operational needs and maximizes visitor satisfaction. This predictive, proactive and adaptive operational intervention capability enables tourism enterprises to allocate resources more efficiently, significantly improve service quality, and create a more positive visitor experience, thus forming a virtuous cycle of continuous optimization.

[0061] The workflow of the system is a data-driven, intelligent decision-making closed-loop process. It first collects structured and unstructured data from various multi-source heterogeneous channels including online platforms, offline consumption, social media, Internet of Things sensors, voice interaction, etc. in real time through the intelligent perception layer. Then, in the data intelligence hub, the system preprocesses and cleans these raw data, and starts the core multi-modal sentiment computing and causal inference unit: this unit uses convolutional neural networks (CNN) to extract deep features from data in different modalities such as text, images, voice, and physiological signals, and through a multi-modal fusion network based on CNN, it realizes the accurate alignment and deep fusion of these features, forming a unified multi-modal representation. On this basis, the system identifies the multi-dimensional fine-grained emotional states of tourists such as satisfaction, anxiety, and fatigue in real time, and uses causal inference models to analyze and quantify the causal relationship between specific operational behaviors and these emotional changes, revealing the deep causes. At the same time, the system constructs a dynamic digital twin combining with data such as passenger flow and facility status, and can achieve cross-enterprise collaboration through federated reinforcement learning. Finally, the intelligent decision-making engine receives real-time tourist emotional data from the data intelligence hub, causal relationship insights of operational behaviors, and dynamic states of the digital twin, and combines pre-set operational goals and constraints to form a comprehensive perception of the current tourism operating environment. Then, it uses the knowledge reasoning engine to match business rules and expert experience, and uses reinforcement learning models to optimize decision-making strategies through continuous learning and trial and error, generating context-aware, personalized, and explainable proactive optimization recommendations. These intelligent shunting and personalized service pushing recommendations are executed and pushed through the service application layer, while the system continuously tracks the execution effect and feeds it back to the decision-making engine, forming a closed-loop learning optimization mechanism to ensure the accuracy, adaptability, and efficiency of the decision-making, and ultimately achieving intelligent and fine-grained management of the tourism industry.

[0062] In addition, in view of the problems of high technical threshold and cost, and data privacy ethics; to reduce the technical threshold and cost: a modular deployment solution can be developed, which splits the system into core functional modules including emotion recognition, causal inference, etc., allowing small and medium-sized tourism enterprises to select functions as needed; a cloud-based mode is launched to reduce the demand for local computing resources, and through a subscription system, the initial investment is reduced; cooperate with edge computing device suppliers to optimize low-cost hardware support to ensure real-time processing capability; provide open source algorithm models to encourage community optimization to reduce development costs. And in solving the problem of data privacy ethics: an explicit tourist data collection authorization mechanism can be established, through the scenic area APP and intelligent devices to send informed consent prompts to tourists, explaining the data use and allowing users to selectively authorize; use federated learning framework to ensure that raw data does not leave the local and only transfer model parameters; implement anonymization processing and comply with data protection regulations such as GDPR; regularly publish transparency reports to disclose data usage and enhance user trust.

[0063] Example Four

[0064] Specific Case Application One:

[0065] Intelligent diversion and experience optimization of peak scenic spot passenger flow: In a well-known historical and cultural scenic spot, passenger flow is excessively concentrated during holidays, leading to long queues, decreased experience, and even potential safety hazards. The system captures real-time passenger density and travel speed in each area through the deployment of cameras and uses convolutional neural networks to analyze passenger facial expressions and body language. Meanwhile, the system connects with the smart guide devices rented by the scenic spot and the smart wearable devices of partners through user authorization, obtaining part of the passengers' heart rate variability (HRV) data as an auxiliary judgment of fatigue and stress. In addition, the system real-time scrapes comments and messages posted by passengers on the scenic spot's exclusive APP and social media, and analyzes verbal complaints at the consultation point through CNN-based voice semantic recognition. The system deeply integrates the above multi-modal data to identify that "the anxiety level of passengers in the main exhibition hall area has significantly increased" and "the fatigue level is generally increasing on the path to XX point." The causal inference model quickly analyzes that "the queue time in the main exhibition hall has exceeded 45 minutes" is the direct cause of "increased passenger anxiety" and "decreased satisfaction," and finds that "the lack of clear alternative route guidance" is an indirect cause of "increased passenger fatigue." Based on this, the system immediately issues a high-level warning, indicating that the main exhibition hall is about to reach the congestion critical point and predicting that the anxiety in the area will further intensify within the next 30 minutes. The intelligent decision-making engine generates and pushes multi-level, personalized intervention suggestions based on pre-set operation goals and real-time resources: for the operation management side, it suggests immediately activating the backup channel, increasing the number of guides in the congested area, and adjusting the release rhythm of the main exhibition hall entrance; for stranded passengers, it pushes messages through the scenic spot APP and electronic screens, suggesting avoiding the peak and choosing VIP fast channel services, and can push rest area coupons and interactive games to highly anxious passengers; for visitors about to enter the park, it displays real-time congestion indexes of each popular area on the entrance screen and recommends less crowded scenic routes. This significantly reduces local congestion in the scenic spot during peak hours, shortens the average queue time by 15-25%, effectively alleviates negative emotions, and improves passenger satisfaction by about 8-12%, making better use of scenic resources.

[0066] Specific Case Application Two:

[0067] Hotel guest personalized service and repeat rate improvement: A certain chain of high-end hotels is committed to improving customer loyalty and repeat rate, but traditional methods are difficult to capture the micro experience and potential needs of guests during their stay in real time. This system analyzes the reviews, messages posted by guests on the hotel APP, online platform, social media, and intelligent guest room voice interaction records made in the hotel. The cameras and microphones in the public areas of the hotel capture the micro-expressions and tone of the guests, identify their emotions such as impatience, satisfaction, and confusion during the service process. The smart access control and guest room control system records the guest's entry and exit time, room facility usage preferences, gym and pool usage frequency, and other behavior data. The system fuses and analyzes multi-modal data to identify "a certain guest has an unsatisfactory emotion about the room temperature adjustment on the second day of stay in the morning" and "a certain guest shows a slight anxious emotion when dining in the restaurant". The causal inference model analyzes the "slow response of the room temperature control system" as the direct cause of the guest's "morning irritability emotion" through the double difference method trained by historical user behavior logs; while "long waiting time for coffee during breakfast peak hours" is the key inducement of "dining anxiety emotion". The system immediately sends a precise reminder to the hotel manager: the guest in a certain room has potential dissatisfaction with the comfort of the room environment, and it is recommended to immediately arrange the engineering department to check the temperature control system, and send the customer service manager to call and provide a humidifier. For the breakfast scene, the system suggests: remind the restaurant staff to pay attention to this guest in the short term, and actively provide fast coffee service; in the long term, it is recommended that the hotel add a self-service coffee machine during the breakfast peak period, because causal inference shows that this is an important factor affecting breakfast satisfaction. The system can also recommend personalized entertainment facilities and nearby activities based on the emotional recognition of guest preferences. This application improves the personalization and immediacy of guest service experience, improves the satisfaction of guests with hotel services by about 10%, effectively reduces the negative review rate, and is expected to increase the repeat rate of guests by 5-8%.

[0068] Specific case application three:

[0069] Travel agency itinerary product iteration and precision marketing: An online travel agency has a large amount of user review and itinerary feedback data, but it is difficult to systematically find product pain points and user deep needs from it, resulting in a lack of precision in new product development and marketing strategies. The system processes a large number of user travel diaries, scenic spot reviews, and customer service conversation records left on the platform, and identifies the fine-grained sentiment and keywords of each link such as service, scenic spot, catering, and accommodation mentioned in the text through CNN-based text sentiment analysis. The system analyzes user-shared travel photos, identifies elements in the photos through image recognition technology, and judges the user's shooting mood in combination with image sentiment recognition. The system analyzes user voice feedback on travel experience in customer service calls, and captures the real demands behind complaints and praise through voice sentiment recognition and semantic analysis. At the same time, the system also analyzes user behavior data such as browsing track, favorites content, purchase history, and itinerary planning preferences on the website. The system fuses and analyzes all data to find that some users of a "classic cultural in-depth tour" line show "anxiety" and "fatigue" in "insufficient free time" and "too fast tour guide speed". The causal inference model analyzes that "insufficient free time caused by a tight schedule" is a significant cause of "general fatigue of in-depth tour tourists"; "the mismatch between the personal style of the tour guide and the acceptance of the tourists" is an indirect cause of "the understanding barrier and anxiety of some tourists". At the same time, it is found that "insufficient local characteristic food recommendations" is the key reason for "some users' lower-than-expected satisfaction with the catering sector". The system pushes a structured insight report to product managers and marketing teams: suggest developing a "cultural in-depth tour with higher flexibility" version, adding at least half a day of free time; for tour guide services, suggest introducing a tourist rating mechanism and considering providing speed adjustment and multilingual interpretation services; for the catering sector, suggest cooperating with local specialty restaurants to provide more customized food experiences. The system can also identify users who have high expectations for "food experience" and are interested in "cultural in-depth tours", and push new cultural routes with rich food elements through personalized recommendation algorithms. This application accurately extracts product pain points and potential needs from massive user data, improving user demand analysis efficiency in the new product development cycle by about 30%; through targeted product optimization and precision marketing, the market acceptance of new route products is expected to increase by 10-15%, and the user repurchase rate is expected to increase by 3-5%.

[0070] Performance comparison of the present invention with existing systems:

[0071]

[0072] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A big data-based tourism industry operation behavior tracking, storing, and analyzing system, characterized in that, Intelligent perception layer: for deploying multi-source heterogeneous data collection probes, real-time collection of multi-modal tourism-related data including structured and unstructured data, the collection process can combine edge computing to achieve real-time processing and preliminary feature extraction; Data intelligence hub: for fusion, preprocessing, storage and management of data collected by the intelligent perception layer, and construction of semantic knowledge system in the tourism field, the core components of the data intelligence hub include: multi-modal sentiment computing and causal inference unit: for performing the following operations: multi-modal data feature extraction and alignment, multi-dimensional sentiment state real-time identification, potential cause and effect relationship inference and experience quality active optimization suggestion generation; Intelligent decision engine: for receiving and executing the optimization suggestions generated by the multi-modal sentiment computing and causal inference unit, and can combine reinforcement learning and simulation for autonomous decision optimization; Service application layer: for providing the emotion analysis results, causal inference report and optimization suggestions to the operators, tourists and other third parties through user interface and API interface. 2.The big data-based tourism industry operation behavior tracking, storing, and analyzing system according to claim 1, characterized in that: In the multi-modal data feature extraction and alignment process, the cross-modal attention mechanism aims to improve the model's attention to key information when fusing different modal information. 3.The big data-based tourism industry operation behavior tracking, storing and analyzing system according to claim 1, characterized in that: The multi-dimensional sentiment state real-time identification unit can identify the micro-sentiment fluctuations and macro-group sentiment trends of tourists. 4.The big data-based tourism industry operation behavior tracking, storing and analyzing system according to claim 1, characterized in that: In the potential cause and effect relationship inference, the causal inference model can distinguish between correlation and causality to avoid misjudgment. 5.The big data-based tourism industry operation behavior tracking, storing and analyzing system according to claim 1, characterized in that: In the experience quality active optimization suggestion generation process, the knowledge reasoning engine and reinforcement learning model can continuously learn and optimize the generation strategy of suggestions based on historical operation data and feedback. 6.The big data-based tourism industry operation behavior tracking, storing and analyzing system according to claims 1-5, characterized in that: The steps include: Sp1: multi-modal tourism data collection: real-time collection of multi-modal tourism-related data including structured and unstructured data; Sp2: multi-modal data feature extraction and alignment: for the collected different modal data, respectively and jointly perform deep feature extraction, and use cross-modal attention mechanism and multi-modal fusion network to accurately align and fuse the extracted multi-modal features in time and semantics; Sp3: multi-dimensional sentiment state real-time identification: based on the fused multi-modal representation, real-time identification of multi-dimensional, fine-grained sentiment state of the tourism users and groups; Sp4: potential cause and effect relationship inference: for the identified sentiment state changes, use causal inference model to analyze and quantify the causal relationship and action path between specific operation behavior and tourist sentiment state, identify the key causes leading to emotional changes; Sp5: experience quality active optimization suggestion generation and execution: according to the sentiment state identification results and potential cause and effect relationship inference results, generate context-aware, personalized and explainable experience quality active optimization suggestions, and execute and push the suggestions through the intelligent decision engine. 7.The big data-based tourism industry operation behavior tracking, storing and analyzing system according to claim 6, characterized in that: The sentiment state identified in Sp3 includes tracking the emotional changes of tourists at different time points and different geographical locations. 8.The big data-based tourism industry operation behavior tracking, storing and analyzing system according to claim 6, characterized in that: The key causes identified in Sp4 include direct causes and indirect causes.

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